Learning Traffic Network Embeddings for Predicting Congestion Propagation

Traffic congestion has become a global concern due to continuous increase in traffic demand and limited road capacity. The ability to predict traffic congestion propagation, which depicts the spatiotemporal evolution of the congestion scenario, is essential for developing smart traffic management systems and enabling road users to make informed route choices. In this work, we study the behavior of congestion propagation at the road segment level, and leverage this to develop a novel machine learning framework that characterizes and predicts the congestion evolution among different road segments in the traffic network. In particular, our framework can infer the likelihood of congestion propagation between any pair of road segments through single or multiple propagation paths. The proposed framework relies on a network embedding module to learn a representation for each road segment, and a propagation model which calculates the congestion propagation likelihood based on the learned representations. Specifically, an asymmetric embedding of local proximity and global tendency (AE-LPGT) is relied upon for learning low dimension embeddings of the road segments which incorporate various realistic properties of congestion propagations, such as the local proximity property, global propagation tendency, and asymmetric transitivity of congestion propagations. Experimental results with Singapore traffic data show that our method significantly outperforms the state-of-the-art, and the congestion propagation properties in our embeddings have significant impact on the prediction performance.